leuk                  package:MASS                  R Documentation

_S_u_r_v_i_v_a_l _T_i_m_e_s _a_n_d _W_h_i_t_e _B_l_o_o_d _C_o_u_n_t_s _f_o_r _L_e_u_k_a_e_m_i_a _P_a_t_i_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     A data frame of data from 33 leukaemia patients.

_U_s_a_g_e:

     data(leuk)

_F_o_r_m_a_t:

     A data frame with columns:

     '_w_b_c' white blood count

     '_a_g' a test result, '"present"' or '"absent"'

     '_t_i_m_e' survival time in weeks

_D_e_t_a_i_l_s:

     Survival times are given for 33 patients who died from acute
     myelogenous leukaemia.  Also measured was the patient's white
     blood cell count at the time of diagnosis.  The patients were also
     factored into 2 groups according to the presence or absence of a
     morphologic characteristic of white blood cells. Patients termed
     AG positive were identified by the presence of Auer rods and/or
     significant granulation of the leukaemic cells in the bone marrow
     at the time of diagnosis.

_S_o_u_r_c_e:

     Cox, D. R. and Oakes, D. (1984) _Analysis of Survival Data_.
     Chapman & Hall, p. 9.

     Taken from

     Feigl, P. & Zelen, M. (1965) Estimation of exponential survival
     probabilities with concomitant information. _Biometrics_ *21*,
     826-838.

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_E_x_a_m_p_l_e_s:

     library(survival)
     plot(survfit(Surv(time) ~ ag, data = leuk), lty = 2:3, col = 2:3)

     # now Cox models
     leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), leuk)
     summary(leuk.cox)

